Services
AI consulting for financial services
AI consulting for financial services lives or dies on the parts a demo skips: regulated data, model risk, and explaining a decision to someone who can fine you. That is the work we do, and it is why we lead with governance, not the model.
Front office, back office, and where to start
It helps to split the work. Front-office AI touches customers and money directly: credit and underwriting decisions, fraud and anti-money-laundering monitoring, advice. It pays well and it carries the most regulatory weight, because a wrong or unexplainable decision is a compliance event, not just a bug. Back-office AI is the safer place to start: reconciliation, reporting, KYC document handling, the manual steps between core systems. Most firms should earn their first wins in the back office and treat every front-office model as a governed system from day one. Part of our job is telling you which side a given idea belongs on.
Model risk and explainability
Financial regulators have expected disciplined model risk management for years, long before generative AI, and that expectation now covers AI models too. In a 2026 speech on AI in the financial system, the Federal Reserve returned to the theme supervisors keep pressing: know what your models do, validate them, and stay accountable for their output. We build to that. Every model gets an owner, a validation trail, monitoring for drift, and a documented reason a human can give a regulator or an auditor. Where it fits, we map controls to the NIST AI RMF rather than inventing a private framework.
How an engagement works
Everything starts with the assessment: fixed scope, 3–6 weeks, $20,000 to $80,000 (where in that range depends on scope: how many systems and teams we assess, company size, and regulatory exposure). You get a plan with the use case, the data boundaries, the model-risk work, and costs attached. Larger build or advisory work follows only if it earns it. Published 2026 ranges put a scoped production build at $50,000–$250,000. And that band is a single scoped build; multi-system, multi-year programs run seven figures and beyond, and scale like that is work we take. The automation-heavy end of this, the document and process work, is its own page: AI automation for financial services.
What is already working in the sector
None of this is speculative. Fraud detection and AML monitoring have used machine learning in banking for years, credit teams use it to speed underwriting, and document-heavy operations are the clearest near-term wins. We wrote up the concrete patterns in AI use cases in finance and AI use cases in banking. What we add is the honest read on which of them fits your data, your regulator, and your risk appetite, and which is a slide that will not survive procurement.
Who does the work
The senior people at Tillerbridge are Nick Major, an engineer, and Isaac Major, an operator. Backgrounds are on the about page. We are a young firm, so there are no bank logos here and we will not borrow any; what we show instead is published pricing and a fixed-scope process. The governance backbone is our AI governance consulting and AI risk assessment work, the wider enterprise realities are on enterprise AI consulting, and this is the financial-services shape of our broader AI consulting.
Questions people ask
- Where should a financial-services firm start with AI?
- Almost always the back office: reconciliation, reporting, KYC and document handling, the manual work between core systems. It is lower risk, the data is easier to bound, and the savings are real. Treat every customer- or credit-facing model as a governed system from the start, and earn the trust to build those on quieter wins first.
- How do you handle model risk and regulators?
- Every model gets an owner, a validation trail, drift monitoring, and a documented, explainable reason for its output, mapped to the NIST AI RMF where it fits. It is the same model-risk discipline supervisors like the Federal Reserve have expected for years, applied to AI rather than invented for it.
- How much does it cost?
- Every engagement starts with a fixed-scope assessment at $20,000 to $80,000 over 3–6 weeks; where in that range depends on scope: how many systems and teams we assess, company size, and regulatory exposure. Larger build or advisory work is priced per scope after that; published 2026 ranges put a scoped production build at $50,000–$250,000, and that band is a single scoped build; multi-system, multi-year programs run seven figures and beyond, and scale like that is work we take. The full market picture is in our AI consulting rates guide.
- Do you build the AI or just advise?
- Both, in that order. The assessment is advice: what is worth building and how to govern it. If a build earns it, the senior people who scoped it do the engineering, on your infrastructure, and you own what we build. We resell no software and take no vendor commissions, so a recommendation has one reason behind it.
Tell us about the work.
A few lines is enough. We read every enquiry ourselves and reply within one business day.